When optical systems such as telescopes or cameras, for example, are used in the atmosphere, disturbances in the optical imaging of the optical system may occur as a result of air disturbances. By way of example, air disturbances can be caused by solar irradiance, which in turn causes air turbulences. Such optical systems are often coupled to cameras or image sensors in order to facilitate an image recording.
Such air disturbances can lead to flickering, i.e. a movement, in the image and/or to unsharpness in the image, which can be observed through the optical system or which can be recorded by way of the optical system.
An unwanted movement in the image may likewise arise if the optical system is inadvertently moved during the image recording, for example as a result of tremors of an employed tripod. Additional disturbances may occur as a result of the scanning movement in the case of image recording devices that operate on a scanning principle, e.g. have a line sensor that records an object line by line or a sensor with a single pixel that scans an object to be recorded. A so-called slit scan laser scanning microscope (LSM) is an example of a so-called line sensor; an electron microscope or conventional laser scanning microscope is an example for recording a single pixel in conjunction with scanning of an object. Such movements can have translational and/or rotational components; i.e., both a shift and a rotation may occur.
Image disturbances that cause a movement, such as the aforementioned flickering or inadvertent movements of the optical system, are also disturbing, in particular, in the case of video recordings as well, i.e. in the case where a plurality of successive images are recorded.
In the case of digital image recording using an image sensor, as is usually used these days, there is the option of improving recorded images by subsequent computer-assisted processing. By way of example, there are various approaches for subsequently correcting unwanted movements or flickering by way of appropriate image processing. In some approaches, the flickering is reduced by registering prominent patterns such as prominent edges, for example, in the image. Expressed differently, prominent edges are determined in a sequence of images, for example in a video, and the images are corrected in such a way that the patterns in subsequent images are aligned. This approach works well if corresponding prominent features, e.g. edges, are present in the images, for example if many houses with windows occur. This becomes difficult in nature recordings with bushes, grass, etc., because prominent edges or other suitable patterns are less present or not present at all. If registering prominent patterns does not work in such a case, this may also have a negative effect on subsequent sharpening of the images should an employed sharpening algorithm be applied to a plurality of successive images.
A further approach is a so-called “speckle imaging”, which is described in C. J. Carrano, “Speckle Imaging over Horizontal Paths”, Lawrence Livermore National Laboratory, Jul. 8, 2002, for example. Further information in this respect was found in e.g. G. Weigelt and B. Wirnitzer, OPTICS LETTERS Vol. 8, No. 7, 1983, p. 389ff or Taylor W. Lawrence et al., OPTICAL ENGINEERING Vol. 31, No. 3, p. 627ff, 1992. The main disadvantage of speckle imaging is that it requires much computational time or corresponding high-power computers. Originally, speckle imaging was mainly used in astronomy where, at least in many applications, there is sufficient time for image post-processing and/or sufficient computational capacity available. For terrestrial recordings, which should often be carried out in real time, the implementation of such an approach on the basis of the speckle imaging at least requires much outlay since specially designed hardware (e.g. ASICs, application-specific integrated circuits) is required, or it may even be impossible. Moreover, moving objects, such as persons running or motor vehicles driving, for example, are processed just like air disturbances in the speckle imaging algorithm. This may lead to artifacts, in which these moving objects are typically blurred or even disappear in part.